ArticleNature communications2024
Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- TransBindpMHCI: a transformer-based model for pan-specific MHC-I peptide binding prediction.BMC bioinformatics · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Single-cell RNA sequencing unveils enhanced antitumor immunity in colorectal cancer with PD-1 blockade and LINC00673 deletion.Biomarker research · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Continued domain-specific pre-training of protein language models for pMHC-I binding prediction.Proceedings of machine learning research · 2025Article
- NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.Frontiers in immunology · 2025Article
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Authors and funding
18 authors.
Funding
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Abstract
Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins. HLApollo provides end-to-end treatment of MHC-I sequences and deconvolution of multi-allelic data, using a negative-set switching strategy to mitigate misassigned negatives in unlabelled ligandome data. HLApollo shows a 12.65% increase in average precision (AP) on ligandome data and a 4.1% AP increase on immunogenicity test data compared to next-best models. Incorporating protein features from protein language models yields further gains and reduces the need for gene expression measurements. Guided by clinical use, we demonstrate pan-allelic generalization which effectively captures rare alleles in underrepresented ancestries.
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